Investigation of Bias in Gene Selection and Development of a Tool to Address Understudied Genes

Core Concepts

The author analyzes the bias in gene selection and proposes a tool, FMUG, to address understudied genes by providing insights into the selection process.

Abstract

The content discusses how present-day research on human genes tends to focus on well-studied genes, neglecting understudied ones. The analysis reveals that despite advancements in high-throughput technologies, understudied genes are often abandoned during the reporting stage. The study introduces FMUG, a tool designed to help scientists identify and investigate overlooked genes. By examining various factors influencing gene selection, the authors shed light on the biases present in current research practices. The investigation highlights that many factors contribute to the neglect of understudied genes, such as availability of plasmids, number of publications about a gene, and loss-of-function intolerance. Through data-driven analysis and literature review, the authors identify 33 factors associated with gene selection across different experimental approaches. The study emphasizes that promoting research on understudied genes can lead to valuable discoveries and enhance our understanding of the human genome. FMUG is introduced as a tool that allows researchers to actively engage with bias during gene selection processes. By providing real-time feedback and customizable filters based on identified factors, FMUG aims to empower scientists to make informed decisions when selecting genes for further study. Overall, this content underscores the importance of addressing bias in gene selection practices to advance genomics research.

Stats

Among 450 GWAS studies analyzed, only 39.6% of hit genes were highlighted in titles/abstracts. In transcriptomics experiments, only 0.88% of hit genes were mentioned in titles/abstracts. For AP-MS studies, 3.93% of hit genes were highlighted in titles/abstracts. In CRISPR screens, only 0.19% of hit genes were mentioned in titles/abstracts.

Quotes

"Understudied genes are systematically abandoned between -omics experiments and result reporting." "Publications focusing on less-investigated genes tend to accumulate more citations." "Authors do not highlight understudied hits in high-throughput studies."

Key Insights Distilled From

Meta-Research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

by Richardson,R... at www.biorxiv.org 03-01-2023

https://www.biorxiv.org/content/10.1101/2023.02.28.530483v3

Deeper Inquiries

How can biases in gene selection impact future scientific discoveries beyond genomics?

Biases in gene selection can have far-reaching implications beyond genomics research. Firstly, these biases can lead to a lack of diversity and representation in research, potentially overlooking crucial genes that play significant roles in various biological processes or diseases. This limitation could hinder the development of targeted therapies or interventions for specific conditions that may be associated with understudied genes. Moreover, biases in gene selection can perpetuate existing knowledge gaps and reinforce historical research patterns. By focusing only on well-studied genes, researchers may miss out on novel insights and breakthroughs that could arise from investigating understudied genes. This narrow focus limits the exploration of new pathways, mechanisms, and potential therapeutic targets that could advance our understanding of complex diseases. Additionally, biases in gene selection can impact interdisciplinary collaborations by influencing the allocation of resources and funding towards already well-established areas of research. This imbalance may deter researchers from exploring innovative ideas or unconventional approaches that could lead to groundbreaking discoveries outside the traditional scope of genomics. In summary, biases in gene selection not only limit the breadth and depth of scientific discoveries within genomics but also impede progress across diverse fields by restricting exploration into uncharted territories within the genome.

How might addressing bias through tools like FMUG influence interdisciplinary collaborations within genomics research?

Addressing bias through tools like FMUG has the potential to foster greater collaboration and cross-disciplinary interactions within genomics research. By providing researchers with a systematic way to identify understudied genes based on various factors such as availability of reagents, number of publications, or functional annotations, FMUG empowers scientists to make more informed decisions about which genes to investigate further. One key way FMUG can influence interdisciplinary collaborations is by promoting transparency and awareness around bias in gene selection. Researchers using FMUG are encouraged to consider a broader range of factors when selecting genes for study rather than relying solely on established norms or popular choices. This shift towards conscious decision-making fosters critical thinking and encourages researchers from different disciplines to engage with a more diverse set of genetic targets. Furthermore, FMUG's ability to customize filters based on specific criteria allows researchers with varying expertise or interests to collaborate effectively. For example, a computational biologist interested in evolutionary aspects may prioritize primate-specific genes while a molecular biologist focused on drug discovery may emphasize druggable targets. By enabling tailored searches based on individual preferences or requirements, FMUG facilitates interdisciplinary collaboration by accommodating diverse perspectives and priorities within genomics research. Overall, addressing bias through tools like FMUG promotes inclusivity, innovation, and open-mindedness among researchers working across different domains within genomics. It encourages collaborative efforts towards uncovering hidden gems among understudied genes that have the potential to drive transformative discoveries at the intersection of various scientific disciplines.

What potential drawbacks or limitations might arise from actively promoting research on understudied genes?

While actively promoting research on understudied genes holds numerous benefits for advancing genomic knowledge and uncovering novel biological insights, there are several potential drawbacks or limitations associated with this approach: Resource Allocation: Actively promoting research on understudied genes may require additional resources such as time, funding,and specialized expertise.This allocation of resources towards less explored areas could divert attention from well-established fields where advancements are more predictable. Balancing resource distribution between studying known versus unknown genes becomes crucial but challenging. Validation Challenges: Investigating unfamiliar genes often presents validation challenges due to limited prior data available.Validation experiments, functional assays,and establishing causal relationships may be more complex for previously unstudied genes comparedto those extensively characterized. Interpretation Complexity: Understandingthe functionand significanceof newly discoveredgenescanbechallengingdue tolackofpriorcontextorcomparative data.Interpreting results,reconciling findingswithexistingknowledgebases,and integratingnewinformationintothebroadergenomiclandscapecanposeinterpretationcomplexities. 4)Publication Bias: The pressure topublishpositivefindingsinresearchcouldleadtopublicationbiaswheresuccessfulstudiesonunderstudie dgenesarehighlightedwhilenegativeornullresultsareunderreported.Thisimbalancecouldestablishanincompletepictureofthegenomiclandscapeandpotentiallymisdirectfutureinvestigations 5)Ethical Considerations: Promotingresearchonunderstud iedgenescouldraisepotential ethicalconsiderationsregardingprivacy,dataownership,anduseoffindings.Particularlywhenexploringdisease-relatedgene s,it’scrucialtoaddressethicalimplicationsaroundgenetictesting,personalizedmedicine,andtherapeuticapplications 6)OverlookedPriorResearch: Intensifyingfocusonunderstud iedgenesmightresultinoverlookingpriorknowledgeorexistingdatapertainingtosimilarorgenesrelatedtargets.Repeatingexperimentsorrevisitingpreviousfindingsthathavebeenundervaluedcouldwastetimeandresourcesifnotproperlyintegratedintothenewresearchefforts 7)ConfirmationBias: Activelypromotingresearcho nunderstud iedgeneshasrisksassociatedwithconfirmationbiaswherebyresearcher smayunconsciouslyseekoutevidenceconfirmatoryoft heirhypothesesratherthanobjectivelyevaluatingallavailabledata.Thiscouldleadtonarrowmindedapproachesthatlimitcriticalthinkingandopennesstoalternativeinterpretations 8)GeneralizationChallenges: Findingsfromstudiesfocusedo nun derstud iedg enesma yno tbege ner aliz ablet othewiderpopulati onsorthereforelimitedinscope.Applyingresultsofuniquelyexploredgene stoothercontextsormodelsmayrequirecarefulconsiderationandre-evaluationtomaintainvalidityandrelevanceacrossdifferentsettings 9)ScientificReproducibility Thefocusonless-exploredareaswithinthegenomecoupledwithvalidationchallengesandsmallersample sizesmayimpactthes cientificreproducibilityofs tudiesthatinvestigateunde rstudi edge nes.Issuesrelat ingtostudydesign,laboratoryprotocols,d ataanalysis,m aycompromise theresults’credibilityandreliability Itisimperativetobalan cethebenefitsofpromoti ngres earchonu nderstu diedg eneswit hthese potentia ldrawbacksan dlimitations.Takingame thodica lappr oach,tailoringstrategiesacc ordingtovaryi ngrese archgoalsa ndadopti ngrigorousmethodologiesca nhelptomiti gateadverseeffectsandenha nceth escientifi cvalueofi nvestigation sonlessexploreddomain softheg nome